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Human activity prediction using temporally-weighted generalized time warping
Wang, Haoran1; Yang, Wankou2; Yuan, Chunfeng3; Ling, Haibin4; Hu, Weiming3
Source PublicationNEUROCOMPUTING
2017-02-15
Volume225Issue:1Pages:139-147
SubtypeArticle
AbstractDifferent from traditional human activity recognition, human activity prediction aims to recognize an unfinished activity, typically in absence of explicit temporal progress status. In this paper, we propose a new human activity prediction approach by extending the recently proposed generalized time warping (GTW) [20], which allows an efficient and flexible alignment of two or more multi-dimensional time series. More specifically, for each activity video, either complete or incomplete, we first decompose it into a sequence of short video segments. Then, we represent each segment by the local spatial-temporal statistics using the classical bag-of visual -words model. In this way, the comparison between a query sequence (i.e., containing an incomplete activity) and a reference sequence (i.e., containing a full activity) boils down to the problem of aligning their corresponding segment sequences. While GTW treats different portions of a sequence as equally important, our task is in favor of early portions since an incomplete activity video always aligns from the beginning of a complete one. Thus motivated, we develop a temporally-weighted GTW (TGTW) algorithm for the activity prediction problem by encouraging alignment in the early portion of an activity sequence. Finally, the similarity derived from TGTW is combined with the k-nearest neighbors algorithm for predicting the activity class of an input sequence. The proposed approach is evaluated on several publicly available datasets in comparison with state-of-the-art approaches. The experimental results and analysis clearly demonstrate the effectiveness of the proposed approach.
KeywordActivity Prediction Time Warping Alignment
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.neucom.2016.11.004
WOS KeywordACTION RECOGNITION ; HUMAN MOTION ; ALIGNMENT ; SEQUENCES
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61603080 ; Fundamental Research Funds for the Central Universities of China(N150403006) ; NSF of Jiangsu Province(BK20140566 ; China Postdoctoral science Foundation(2014M561586) ; 61473086 ; BK20150470) ; 61375001)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000392164400014
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14360
Collection模式识别国家重点实验室_视频内容安全
Affiliation1.Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China
2.Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA
Recommended Citation
GB/T 7714
Wang, Haoran,Yang, Wankou,Yuan, Chunfeng,et al. Human activity prediction using temporally-weighted generalized time warping[J]. NEUROCOMPUTING,2017,225(1):139-147.
APA Wang, Haoran,Yang, Wankou,Yuan, Chunfeng,Ling, Haibin,&Hu, Weiming.(2017).Human activity prediction using temporally-weighted generalized time warping.NEUROCOMPUTING,225(1),139-147.
MLA Wang, Haoran,et al."Human activity prediction using temporally-weighted generalized time warping".NEUROCOMPUTING 225.1(2017):139-147.
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